Word Sense Disambiguation (WSD) is the task of identifying the sense, or meaning, that an ambiguous word takes in a specific context. Recent WSD models [1]}, [2]}, [3]} have made large gains on the task, surpassing the estimated 80% F1 human performance upper bound on WordNet annotated corpora [4]}.
Despite this breakthrough, the task remains far from solved: performance on rare and zero-shot senses is still low, and in general, current WSD models struggle to learn senses with few training examples [5]}, [2]}. This performance gap stems from limited data for rare senses in current WSD datasets, which are annotated on natural language documents that contain a Zipfian distribution of senses [7]}.
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